{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 图像的储存模型是什么样的？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe9db59f9b0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "tiny = [\n",
    "    [0, 20, 30, 150, 120],\n",
    "    [200, 200, 250, 70, 3],\n",
    "    [50, 180, 85, 40, 90],\n",
    "    [240, 100, 50, 255, 10],\n",
    "    [30, 0, 75, 190, 220]    \n",
    "]\n",
    "\n",
    "plt.matshow(tiny,cmap='gray')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
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